Dynamic Texture Recognition Using Multiscale Binarized Statistical Image Features
Dynamic Texture Recognition Using Multiscale Binarized Statistical Image Features
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DOI：
10.1109/tmm.2014.2362855
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发表时间：
2014-10
影响因子：
7.3
通讯作者：
Shervin Rahimzadeh Arashloo;J. Kittler
中科院分区：
文献类型：
--
作者：
Shervin Rahimzadeh Arashloo;J. Kittler
本文提出了一种用于时变纹理表示和识别的时空描述子[三个正交平面上的二值化统计图像特征（BSIF-TOP）]。描述符，在精神上类似于众所周知的本地二进制模式的三个正交平面上的方法，估计直方图的二进制编码的图像序列的三个正交平面上对应的空间/时空维度。然而，与以启发式方式生成代码的一些其他方法不同，BSIF-TOP方法中的二进制代码生成是通过对空间/时空支持的不同区域进行滤波操作以及通过将滤波器响应二进制化来实现的。在使用白化变换进行预处理之后，通过在三个平面中的每一个上的独立分量分析来学习滤波器。通过将BSIF-TOP描述符扩展到多分辨率方案，描述符能够在多个尺度上捕获图像序列的时空内容，提高其表示能力。在UCLA、Dyntex和Dyntex++动态纹理数据库上的实验结果表明，该方法与现有方法相比具有很好的性能。
A spatio-temporal descriptor for representation and recognition of time-varying textures is proposed [binarized statistical image features on three orthogonal planes (BSIF-TOP)] in this paper. The descriptor, similar in spirit to the well known local binary patterns on three orthogonal planes approach, estimates histograms of binary coded image sequences on three orthogonal planes corresponding to spatial/spatio-temporal dimensions. However, unlike some other methods which generate the code in a heuristic fashion, binary code generation in the BSIF-TOP approach is realized by filtering operations on different regions of spatial/spatio-temporal support and by binarizing the filter responses. The filters are learnt via independent component analysis on each of three planes after preprocessing using a whitening transformation. By extending the BSIF-TOP descriptor to a multiresolution scheme, the descriptor is able to capture the spatio-temporal content of an image sequence at multiple scales, improving its representation capacity. In the evaluations on the UCLA, Dyntex, and Dyntex++ dynamic texture databases, the proposed method achieves very good performance compared to existing approaches.